Zobrazeno 1 - 10
of 38 279
pro vyhledávání: '"A, Filippi"'
The era of exascale computing presents both exciting opportunities and unique challenges for quantum mechanical simulations. While the transition from petaflops to exascale computing has been marked by a steady increase in computational power, the sh
Externí odkaz:
http://arxiv.org/abs/2409.11881
Autor:
Deur, A., Kuhn, S. E., Ripani, M., Zheng, X., Acar, A. G., Achenbach, P., Adhikari, K. P., Alvarado, J. S., Amaryan, M. J., Armstrong, W. R., Atac, H., Avakian, H., Baashen, L., Baltzell, N. A., Barion, L., Bashkanov, M., Battaglieri, M., Benkel, B., Benmokhtar, F., Bianconi, A., Biselli, A. S., Booth, W. A., ossu, F. B, Bosted, P., Boiarinov, S., Brinkmann, K. Th., Briscoe, W. J., Bueltmann, S., Burkert, V. D., Carman, D. S., Chatagnon, P., Chen, J. P., Ciullo, G., Cole, P. L., Contalbrigo, M., Crede, V., D'Angelo, A., Dashyan, N., De Vita, R., Defurne, M., Diehl, S., Djalali, C., Drozdov, V. A., Dupre, R., Egiyan, H., Alaoui, A. El, Fassi, L. El, Elouadrhiri, L., Eugenio, P., Faggert, J. C., Fegan, S., Fersch, R., Filippi, A., Gates, K., Gavalian, G., Gilfoyle, G. P., Gothe, R. W., Guo, L., Hakobyan, H., Hattawy, M., Hauenstein, F., Heddle, D., Hobart, A., Holtrop, M., Ireland, D. G., Isupov, E. L., Jiang, H., Jo, H. S., Joosten, S., Kang, H., Keith, C., Khandaker, M., Kim, W., Klein, F. J., Klimenko, V., Konczykowski, P., Kovacs, K., Kripko, A., Kubarovsky, V., Lanza, L., Lee, S., Lenisa, P., Li, X., Long, E., MacGregor, I. J. D., Marchand, D., Mascagna, V., Matamoros, D., McKinnon, B., Meekins, D., Migliorati, S., Mineeva, T., Mirazita, M., Mokeev, V., Munoz-Camacho, C., Nadel-Turonski, P., Nagorna, T., Neupane, K., Niccolai, S., Osipenko, M., Ostrovidov, A. I., Pandey, P., Paolone, M., Pappalardo, L. L., Paremuzyan, R., Pasyuk, E., Paul, S. J., Phelps, W., Phillips, S. K., Pierce, J., Pilleux, N., Pokhrel, M., Price, J. W., Prok, Y., Radic, A., Reed, T., Richards, J., Rosner, G., Rossi, P., Rusova, A. A., Salgado, C., Schmidt, A., Schumacher, R. A., Sharabian, Y. G., Shirokov, E. V., Shrestha, U., Sirca, S., Sparveris, N., Spreafico, M., Stepanyan, S., Strakovsky, I. I., Strauch, S., Sulkosky, V., Tan, J. A., Tenorio, M., Trotta, N., Tyson, R., Ungaro, M., Upton, D. W., Vallarino, S., Venturelli, L., Voskanyan, H., Voutier, E., Watts, D. P., Wei, X., Wood, M. H., Zachariou, N., Zhang, J., Zurek, M.
The spin structure functions of the proton and the deuteron were measured during the EG4 experiment at Jefferson Lab in 2006. Data were collected for longitudinally polarized electron scattering off longitudinally polarized NH$_3$ and ND$_3$ targets,
Externí odkaz:
http://arxiv.org/abs/2409.08365
For many machine learning methods, creating a model requires setting a parameter that controls the model's capacity before training, e.g.~number of neurons in DNNs, or inducing points in GPs. Increasing capacity improves performance until all the inf
Externí odkaz:
http://arxiv.org/abs/2408.07588
Autor:
Luchetti, Nicole, Smith, Keith M., Matarrese, Margherita A. G., Loppini, Alessandro, Filippi, Simonetta, Chiodo, Letizia
Living systems rely on coordinated molecular interactions, especially those related to gene expression and protein activity. The Unfolded Protein Response is a crucial mechanism in eukaryotic cells, activated when unfolded proteins exceed a critical
Externí odkaz:
http://arxiv.org/abs/2407.12464
Autor:
Crispino, Anna, Nicoletti, Martina, Loppini, Alessandro, Gizzi, Alessio, Chiodo, Letizia, Cherubini, Christian, Filippi, Simonetta
Developing new methods for predicting electromagnetic instabilities in cardiac activity is of primary importance. However, we still need a comprehensive view of the heart's magnetic activity at the tissue scale. To fill this gap, we present a model o
Externí odkaz:
http://arxiv.org/abs/2406.20084
Autor:
CLAS Collaboration, Hobart, A., Niccolai, S., Čuić, M., Kumerički, K., Achenbach, P., Alvarado, J. S., Armstrong, W. R., Atac, H., Avakian, H., Baashen, L., Baltzell, N. A., Barion, L., Bashkanov, M., Battaglieri, M., Benkel, B., Benmokhtar, F., Bianconi, A., Biselli, A. S., Boiarinov, S., Bondi, M., Booth, W. A., Bossù, F., Brinkmann, K. -Th., Briscoe, W. J., Brooks, W. K., Bueltmann, S., Burkert, V. D., Cao, T., Capobianco, R., Carman, D. S., Chatagnon, P., Ciullo, G., Cole, P. L., Contalbrigo, M., D'Angelo, A., Dashyan, N., De Vita, R., Defurne, M., Deur, A., Diehl, S., Dilks, C., Djalali, C., Dupre, R., Egiyan, H., Alaoui, A. El, Fassi, L. El, Elouadrhiri, L., Fegan, S., Filippi, A., Fogler, C., Gates, K., Gavalian, G., Gilfoyle, G. P., Glazier, D., Gothe, R. W., Gotra, Y., Guidal, M., Hafidi, K., Hakobyan, H., Hattawy, M., Hauenstein, F., Heddle, D., Holtrop, M., Ilieva, Y., Ireland, D. G., Isupov, E. L., Jiang, H., Jo, H. S., Joo, K., Kageya, T., Kim, A., Kim, W., Klimenko, V., Kripko, A., Kubarovsky, V., Kuhn, S. E., Lanza, L., Leali, M., Lee, S., Lenisa, P., Li, X., MacGregor, I. J. D., Marchand, D., Mascagna, V., Maynes, M., McKinnon, B., Meziani, Z. E., Migliorati, S., Milner, R. G., Mineeva, T., Mirazita, M., Mokeev, V., Camacho, C. Muñoz, Nadel-Turonski, P., Naidoo, P., Neupane, K., Niculescu, G., Osipenko, M., Pandey, P., Paolone, M., Pappalardo, L. L., Paremuzyan, R., Pasyuk, E., Paul, S. J., Phelps, W., Pilleux, N., Pokhrel, M., Rafael, S. Polcher, Poudel, J., Price, J. W., Prok, Y., Reed, T., Richards, J., Ripani, M., Ritman, J., Rossi, P., Golubenko, A. A., Salgado, C., Schadmand, S., Schmidt, A., Scott, Marshall B. C., Seroka, E. M., Sharabian, Y. G., Shirokov, E. V., Shrestha, U., Sparveris, N., Spreafico, M., Stepanyan, S., Strakovsky, I. I., Strauch, S., Tan, J. A., Trotta, N., Tyson, R., Ungaro, M., Vallarino, S., Venturelli, L., Tommaso, V., Voskanyan, H., Voutier, E., Watts, D. P, Wei, X., Williams, R., Wood, M. H., Xu, L., Zachariou, N., Zhang, J., Zhao, Z. W., Zurek, M.
Measuring Deeply Virtual Compton Scattering on the neutron is one of the necessary steps to understand the structure of the nucleon in terms of Generalized Parton Distributions (GPDs). Neutron targets play a complementary role to transversely polariz
Externí odkaz:
http://arxiv.org/abs/2406.15539
Variational logistic regression is a popular method for approximate Bayesian inference seeing wide-spread use in many areas of machine learning including: Bayesian optimization, reinforcement learning and multi-instance learning to name a few. Howeve
Externí odkaz:
http://arxiv.org/abs/2406.00713
Autor:
Slootman, Emiel, Poltavsky, Igor, Shinde, Ravindra, Cocomello, Jacopo, Moroni, Saverio, Tkatchenko, Alexandre, Filippi, Claudia
Publikováno v:
J. Chem. Theory Comput. 2024, 20, 6020-6027
Quantum Monte Carlo (QMC) is a powerful method to calculate accurate energies and forces for molecular systems. In this work, we demonstrate how we can obtain accurate QMC forces for the fluxional ethanol molecule at room temperature by using either
Externí odkaz:
http://arxiv.org/abs/2404.09755
Autor:
Ijishakin, Ayodeji, Martin, Sophie, Townend, Florence, Agosta, Federica, Spinelli, Edoardo Gioele, Basaia, Silvia, Schito, Paride, Falzone, Yuri, Filippi, Massimo, Cole, James, Malaspina, Andrea
Publikováno v:
Deep Generative Models for Health Workshop, NeurIPS 2023
Brain age prediction models have succeeded in predicting clinical outcomes in neurodegenerative diseases, but can struggle with tasks involving faster progressing diseases and low quality data. To enhance their performance, we employ a semi-supervise
Externí odkaz:
http://arxiv.org/abs/2402.09137
This work develops a Bayesian non-parametric approach to signal separation where the signals may vary according to latent variables. Our key contribution is to augment Gaussian Process Latent Variable Models (GPLVMs) to incorporate the case where eac
Externí odkaz:
http://arxiv.org/abs/2402.09122